IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v183y2024ics0960077924004764.html
   My bibliography  Save this article

Node importance evaluation method of complex network based on the fusion gravity model

Author

Listed:
  • Guo, Haoming
  • Wang, Shuangling
  • Yan, Xuefeng
  • Zhang, Kecheng

Abstract

The study of complex networks is increasingly attracting widespread attention, and the importance analysis of key nodes with significant influence has always been a core problem in network science. Currently, many centrality measures and gravity model methods usually only focus on a single attribute of a node to evaluate its importance. However, they often ignore the influence of node multi-attribute characteristics on the evaluation results. In order to analyze influential nodes in complex networks more effectively, the local and global information of nodes must be fully considered. To solve this problem, based on the multi attribute characteristics of nodes and the gravity model, we propose a new fusion gravity model that fuses the multi attribute characteristics of nodes to evaluate the influential nodes in the network more comprehensively. The model takes into account the local information of the network reflected by the node degree value, the global information of the network reflected by the weight of the maximum eigenvector of the node, and the location information reflected by the node K-shell value. Finally, we used the SI (Susceptible-Infection) propagation model to conduct simulation experiments based on six real network datasets, compared with the traditional centrality method and similar methods, and verified the rationality and excellence of the proposed method.

Suggested Citation

  • Guo, Haoming & Wang, Shuangling & Yan, Xuefeng & Zhang, Kecheng, 2024. "Node importance evaluation method of complex network based on the fusion gravity model," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
  • Handle: RePEc:eee:chsofr:v:183:y:2024:i:c:s0960077924004764
    DOI: 10.1016/j.chaos.2024.114924
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924004764
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2024.114924?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ai, Jun & He, Tao & Su, Zhan & Shang, Lihui, 2022. "Identifying influential nodes in complex networks based on spreading probability," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    2. Yin, Haofei & Zhang, Aobo & Zeng, An, 2023. "Identifying hidden target nodes for spreading in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    3. Wang, Shuliang & Lv, Wenzhuo & Zhang, Jianhua & Luan, Shengyang & Chen, Chen & Gu, Xifeng, 2021. "Method of power network critical nodes identification and robustness enhancement based on a cooperative framework," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    4. Hosni, Adil Imad Eddine & Li, Kan & Ahmad, Sadique, 2020. "Analysis of the impact of online social networks addiction on the propagation of rumors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    5. Wang, Feifei & Sun, Zejun & Gan, Quan & Fan, Aiwan & Shi, Hesheng & Hu, Haifeng, 2022. "Influential node identification by aggregating local structure information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    6. Zhao, Shuying & Sun, Shaowei, 2023. "Identification of node centrality based on Laplacian energy of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    7. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    8. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
    9. Ma, Ling-ling & Ma, Chuang & Zhang, Hai-Feng & Wang, Bing-Hong, 2016. "Identifying influential spreaders in complex networks based on gravity formula," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 205-212.
    10. Yang, Pingle & Meng, Fanyuan & Zhao, Laijun & Zhou, Lixin, 2023. "AOGC: An improved gravity centrality based on an adaptive truncation radius and omni-channel paths for identifying key nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Namtirtha, Amrita & Dutta, Animesh & Dutta, Biswanath, 2018. "Identifying influential spreaders in complex networks based on kshell hybrid method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 310-324.
    2. Zhe Li & Xinyu Huang, 2023. "Identifying Influential Spreaders Using Local Information," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    3. Li, Hanwen & Shang, Qiuyan & Deng, Yong, 2021. "A generalized gravity model for influential spreaders identification in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    4. Wang, Min & Li, Wanchun & Guo, Yuning & Peng, Xiaoyan & Li, Yingxiang, 2020. "Identifying influential spreaders in complex networks based on improved k-shell method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    5. Yu, Senbin & Gao, Liang & Xu, Lida & Gao, Zi-You, 2019. "Identifying influential spreaders based on indirect spreading in neighborhood," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 418-425.
    6. Wang, Zhixiao & Zhao, Ya & Xi, Jingke & Du, Changjiang, 2016. "Fast ranking influential nodes in complex networks using a k-shell iteration factor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 171-181.
    7. Zareie, Ahmad & Sheikhahmadi, Amir, 2019. "EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 141-155.
    8. Wang, Juan & Li, Chao & Xia, Chengyi, 2018. "Improved centrality indicators to characterize the nodal spreading capability in complex networks," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 388-400.
    9. Xu, Shuang & Wang, Pei, 2017. "Identifying important nodes by adaptive LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 654-664.
    10. Liu, Panfeng & Li, Longjie & Fang, Shiyu & Yao, Yukai, 2021. "Identifying influential nodes in social networks: A voting approach," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    11. Bao, Zhong-Kui & Ma, Chuang & Xiang, Bing-Bing & Zhang, Hai-Feng, 2017. "Identification of influential nodes in complex networks: Method from spreading probability viewpoint," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 391-397.
    12. Xu, Guiqiong & Meng, Lei, 2023. "A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    13. Wang, Junyi & Hou, Xiaoni & Li, Kezan & Ding, Yong, 2017. "A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 475(C), pages 88-105.
    14. Zhang, Dayong & Men, Hao & Zhang, Zhaoxin, 2024. "Assessing the stability of collaboration networks: A structural cohesion analysis perspective," Journal of Informetrics, Elsevier, vol. 18(1).
    15. Sun, Hong-liang & Chen, Duan-bing & He, Jia-lin & Ch’ng, Eugene, 2019. "A voting approach to uncover multiple influential spreaders on weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 303-312.
    16. Ai, Jun & He, Tao & Su, Zhan, 2023. "Identifying influential nodes in complex networks based on resource allocation similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 627(C).
    17. Yeruva, Sujatha & Devi, T. & Reddy, Y. Samtha, 2016. "Selection of influential spreaders in complex networks using Pareto Shell decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 133-144.
    18. Huang, Binchao & Yang, Jin-Xuan & Li, Xin, 2021. "Identifying influential links to control spreading of epidemics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    19. Yin, Likang & Zheng, Haoyang & Bian, Tian & Deng, Yong, 2017. "An evidential link prediction method and link predictability based on Shannon entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 699-712.
    20. Ibnoulouafi, Ahmed & El Haziti, Mohamed, 2018. "Density centrality: identifying influential nodes based on area density formula," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 69-80.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:183:y:2024:i:c:s0960077924004764. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.